Name
Title | ||
|---|---|---|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. |
Abstract | ||
|---|---|---|
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Machine Learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Nichol | 1 | 0 | 0.68 |
| Prafulla Dhariwal | 2 | 0 | 0.34 |
| Aditya Ramesh | 3 | 0 | 1.01 |
| Pranav Shyam | 4 | 0 | 0.34 |
| Pamela Mishkin | 5 | 0 | 0.34 |
| McGrew, Bob | 6 | 64 | 4.16 |
| Ilya Sutskever | 7 | 25814 | 1120.24 |
| Mark Chen | 8 | 0 | 1.35 |